The Analytics Life Cycle
DataOps • Artificial Intelligence • ModelOps
Organizations have been pouring money into analytics initiatives for years, but too few are seeing a payoff because models aren't making it into production. To enable data-driven decisions at scale, the analytics life cycle must be highly operational and seamless. By connecting all aspects of the analytics life cycle – DataOps, artificial intelligence and ModelOps – SAS helps you turn your critical questions into trusted decisions and gain real value from your analytics investments.
The Analytics Life Cycle
DataOps
Borrowing from agile software development practices, DataOps provides an agile approach to data access, quality, preparation and governance. It enables greater reliability, adaptability, speed and collaboration in your efforts to operationalize data and analytic workflows.
Access
Access data, regardless of size or complexity.
Prepare
Transform raw data, including AI-powered suggestions.
Visualize
View important relationships in data and share insights.
Govern
Build trust in data, understand lineage and gain transparency.
The Analytics Life Cycle
Artificial Intelligence
Data scientists use a combination of techniques to understand the data and build predictive models. They use statistics, machine learning, deep learning, natural language processing, computer vision, forecasting, optimization and other techniques to answer real-world questions.
Model
Build models with multiple AI techniques to solve real-world problems.
Automate
Automate manual tasks for feature engineering and model tuning.
Collaborate
Enable users with different skill sets to collaborate on solving analytic problems.
Integrate
Work smarter using SAS Viya and open source analytics.
The Analytics Life Cycle
ModelOps
ModelOps focuses on getting AI models through validation, testing and deployment phases as quickly as possible while ensuring quality results. It also focuses on ongoing model monitoring, retraining and governance to ensure peak performance and transparent decisions.
Validate
Ensure models will perform as expected in the real world.
Deploy
Embed models into operational systems and monitor them.
Govern
Ensure decisions are safe and transparent over the life of the model.
Embed
Integrate business rules to ensure up-to-real-time results.
